Digital Watermarking

Introduction Relation to Cryptography –Cryptography is Reversibility (no evidence) Established –Watermarking (1990s) Non-reversible (noise) –Information Hiding Covert communication channel (steganography)

Digital Watermarking Media Video Audio Images –Our discussion will focus on this. Watermarking Algorithm Watermarked Image Original Image Watermark Block Diagram of image watermarking

Applications Copyright –The objective is to permanently and unalterably mark the image so that the credit or assignment is beyond dispute. Digital Rights –A file may only be used by users with a license that matches the watermarked signature. Information Hiding –Foil counterfeiters Revision History –Tamper detection Meta-tagging –Store keywords, descriptions, time along with images.

Criteria Main Criteria –Capacity –Payload –Computational Complexity –Transparency –Robustness Require optimum relationship

Capacity The ability to detect watermarks with a low probability of error as the number of watermarks in a single image increases.

Payload The amount of information that can be legitimately stored within a data stream –Dependent on host medium –JPEG example

Computational Complexity Difficulty in process of watermark extraction –Realtime?

Transparency Transparency refers to the perceptual quality of the data being protected. –Watermark should be invisible over all image types as well as local image characteristics. Need to consider perceptually insignificant portion of host image for insertion for maximum transparency

Robustness Resistance to attacks on the watermark –Attack – an operation performed on the image that compromises the watermark –Active, Passive, Collusion, Forgery –Blind vs. Nonblind Use of non-robust watermarks –eg. tamper detection

Approaches and Implementation Two Types of Encoding –Spatial watermarking (spatial domain) –Spectral watermarking (frequency-domain) Many types due to variety of transforms Adjustments made in frequency domain More robust

Spatial-Domain Implementation Low-level Encoding Use of Image Analysis Operations –eg. Edge Detection/Color Separation Cons –Easily Attacked (Cropping)

Frequency-Domain Implementation Algorithm –Decomposition of image –Addition of Watermark Possibly encoded/encrypted –Re-composition of Image

Frequency-Domain Implementation (Discrete Cosine Transform) Discrete Cosine Transform (DCT) –Used in today’s standard JPEG compression Relation to DFT Compression explained by previous groups –Image divided into non-overlapping blocks –Each block is DC transformed –Block coefficients are quantized through a special algorithm Not ideal for human visual system

Frequency-Domain Implementation (Wavelet Transform) Wavelet Transform –Based on Short Time Fourier Transform (STFT) –Becoming more common in compression techniques Better model of Human Visual System than DCT

Examples of Wavelets

Frequency-Domain Implementation (Common Wavelet Transform Algorithm - Decomposition) Filter Bank Decomposition (10 Bands)

Frequency-Domain Implementation (Wavelet Transform Algorithm - Overview) Watermarked Image Encoded Watermark

Frequency-Domain Implementation (Cortex Transform) Cortex Transform –Recent –Mimics human visual system Corresponds to known structure of human eye –Has its own disadvantages Computational complexity – requires much more data!

Other Issues Just Noticeable Difference (JND) –Threshold based on Human Visual System Adjustment in Frequency Adjustments in Intensity –Important impact on transparency Spatial adjustment of Frequency-Domain Watermark

Spread Spectrum Used to fulfill transparency criterion The watermark in is based on spread spectrum communications –Delivers narrowband data through a noisy channel, by modulating each data symbol with a wideband (but very low amplitude) signal. –The data is a single bit – a yes or no decision on whether the given watermark is present. –The channel is the image data itself –The wideband signal is the watermark.

Color Images Scheme nearly identical to grayscale –R/G/B channels Each color plane treated as a separate image –Luminance/Chrominance channels Luminance = intensity Chrominance = color

Resources ftp://skynet.ecn.purdue.edu/pub/dist/delp/watermark-proceedings/paper.pdf Mihcak, Mehmet Kivanc. “Information Hiding Codes and Their Applications to Images and Audio”, PhD Thesis